System and method for category management

ABSTRACT

The present invention discloses a method, a system and a computer program product for Autonomous sourcing and Category management. The invention includes demand sensing and generation through a category workbench interface providing actionable insights for sourcing operation. The invention includes an AI engine configured for recommending a sourcing strategy through prediction analysis and auto negotiation in sourcing operation of Supply chain.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 16/938,112 which was filed with the United States Patent andTrademark Office on Jul. 24, 2020, the entire contents of which isherein incorporated.

BACKGROUND 1. Technical Field

The present invention relates generally to autonomous Sourcing in supplychain. More particularly, the invention relates to systems, methods andcomputer program product for autonomous sourcing and category managementin supply chain.

2. Description of the Prior Art

Supply chain management professionals are tasked with managing humongousvolumes of data and stakeholders across multiple systems, all whiletrying to build and execute a high performing and resilient strategy. Ithas become humanly impossible to gather all the resources and stay ontop of all the information floating around, let alone analyzing it todraw meaningful insights or make informed decisions.

Procurement as part of Supply chain function, is essential as it impactsand manages organizational spend. If an organization does not have astrategic approach to its procurement needs whether it be procurementfor business continuity e.g. inventory purchasing, or procuring forbusiness support e.g. IT, the spending can get out of control and becomedamaging.

Organizations invest a lot in acquiring skilled Sourcing Managers andCategory Managers who spend tremendous amount of time today to performkey activities like arriving at category plans, positioning andsegmentation, creating & executing strategy execution plans, managecategory performance, drive category governance and manage stakeholderexpectations.

Dependency on Category managers is high to undertake strategic approachto procurement, where the organization segments its spending on goodsand services in different category as part of category management. Thesegmentation arranges goods and services in discrete groups depending onthe functions of these goods and services. Some of the categoriesinclude office management, HR, Professional Services, Security, IT,Transport, travel, medical, industrial products and services etc.

Category management relies on efficient analysis of organizationalstrategic goals and sourcing. Certain factors like Updated pricinganalysis on local and international markets, and the prevailing trends,Supplier performance data, analysis of any saving gained throughnegotiations, substitution and compliance, an updated analysis oforganizational spend in comparison to market data as well as identifyingKey performance indicators for determining areas of improvement, areextremely essential and quantifying data associated with such factorsfor assessment by category managers is not possible.

Further, the problem of availing data for carrying out the tasks andmaking decisions has been replaced with delivery of accurate, clean andtimely information in a structured and sophisticated manner. In additionto that, if the domain of information is large like in case of supplychain, different users will have the requirement of slicing and dicingthe data according to specific needs of the Category.

Recognizing the demand for materials or services before it arises andhaving a good sense of inventory with contractual agreements can helpset the pace. The key here is structured and unstructured informationthat can help define the scope and baseline. While there are severalmethods and systems that deal with structured and unstructured data, theapproach required for a Supply chain related Sourcing process isconsiderably different due to the unknowns. Sourcing and categorymanagement process involves analysis of data with considerabledifference in the variance levels itself. Since, the number ofparameters to be factored in these processes itself changes dynamically,the reliance on human assessment at multiple levels is also risky. Evenwhen computing systems and its processing capabilities are used, theresults are inaccurate due the underlining uncertainty about theinformation being processed. While, the techniques for automatedprocesses are obsolete, less accurate and time consuming, processing ofcertain parameters to ensure efficient sourcing and category managementare never considered.

In view of the above problems, there is a need for system and method ofdata processing for sourcing and category management in supply chainthat can overcome the problems associated with the prior arts.

SUMMARY

According to an embodiment, the present invention provides a method ofautonomous sourcing in supply chain management. The method includesreceiving a demand from at least one data source, triggering a sourcingmodule through a category workbench user interface for initiating atleast one task based on the received demand, processing by an AI enginecoupled to a processor, a plurality of historical data from a data lakebased on one or more data models to generate code for a recommendedstrategy through prediction analysis, injecting by an intelligent bot,aggregated data patterns related to one or more object categories intothe recommended sourcing strategy for generating at least one objectcharacteristic data set. The method also includes identifying one ormore suppliers for executing the recommended strategy based on theobject characteristic data set; and encapsulating one or morerecommended awarding scenario on the category workbench user interfacefor selection. The method includes receiving a response to aquestionnaire based on the object characteristic data set from one ormore recommended suppliers for identifying the one or more suppliers.The questionnaire is generated by the AI engine configured to process ahistorical query knowledge database based on a plurality of parametersand the object characteristic data set. The method also includes thestep of injecting by a bot, one or more impact parameters capable ofmodifying at least one of the actionable insights, the recommendedstrategy, the data patterns or the awarding scenario. Further, themethod includes recommending a negotiation strategy through anauto-negotiator based on a negotiation script generated by the AI enginewherein the awarding scenario is encapsulated based on execution of thenegotiation strategy.

In an embodiment the bot is configured to generate backend scripts basedon the recommended strategy for injecting the aggregated data using AIbased dynamic processing logic to generate the object characteristicdata set.

In an embodiment, the present invention includes a system for sourcingin supply chain management. The system includes a category workbenchapplication user interface configured for triggering a sourcing moduleto initiate at least one task based on a received demand from at leastone data source, an AI engine coupled to a processor and configured forprocessing a plurality of historical data from a data lake based on oneor more data models to generate code for a recommended strategy throughprediction analysis, a data model database for storing one or more datamodels configured for generating the recommended strategy throughprediction analysis and aggregated data patterns related to one or moreobject categories, and a controller encoded with instructions enablingthe controller to function as a bot for injecting the data patterns intothe recommended strategy for generating at least one objectcharacteristic data set, wherein the processor is configured to processthe object characteristic data set to identify one or more suppliers forexecuting the recommended strategy, wherein one or more recommendedawarding scenario is encapsulated on the category workbench applicationuser interface by the bot for selection.

In an embodiment, the present invention provides a Category managementsystem for supply chain operations. The system includes a categoryworkbench application user interface configured to generate a pluralityof data patterns related to one or more object categories for providingactionable insights to a user through at least one dashboard of theinterface, an intelligent bot configured for injecting the data patternsinto at least one recommended strategy for generating at least oneobject characteristic data set. The system includes a processorconfigured to process historical data from a data lake and the objectcharacteristic data set to identify one or more suppliers for executingthe recommended strategy, wherein the actionable insight includes a setof qualitative and quantitative data generated by processing ofhistorical data from a data lake to analyze trends in supply chain forenabling execution of at least one task.

In an embodiment, the present invention provides a category workbenchapplication user interface configured to provide the actionable insightsinto the one or more data patterns being selectable to trigger anapplication associated with each of the one or more data patterns andenable the selected data pattern to be seen within the application andproviding details on spend category, supplier regions spend, actual/vstarget spend, top cost drivers and strategies.

In an embodiment, the invention provides a method of Category managementfor supply chain operations. The method includes generating a pluralityof data patterns related to one or more object categories for providingactionable insights to a user through at least one dashboard of acategory workbench application user interface, injecting by anintelligent bot, the data patterns related to one or more objectcategories into a recommended strategy for generating at least oneobject characteristic data set. The method includes the step ofprocessing historical data from a data lake and the objectcharacteristic data set to identify one or more suppliers for executingthe recommended strategy and generating a set of quantitative andqualitative data on the dashboard to analyze trends in supply chain forenabling execution of at least one task initiated by a user through theinterface.

In an embodiment, the category management method includes initiatingautomated tactical execution process based on the recommended strategywherein the recommendation is auto-flipped into projects with apre-populated responsibility assignment matrix, a savings target, one ormore impacted categories and a supplier data.

In another embodiment the category management method includesencapsulating one or more awarding scenario on the category workbenchapplication user interface by the bot wherein an AI engine incorporatesrules and target constraints including preferable number of suppliers,preferential awards to incumbent suppliers, minimum lead times, andsavings goals to automatically arrive at a most efficient cost forexecuting recommended strategy.

In yet another example embodiment, the data patterns include pricing andspecification information for target pricing, constraints related to oneor more object categories etc.

In an exemplary embodiment, the object categories include category ofitems or service such as office supplies for tables or chairs, ITequipment for laptop or processor etc.

In an embodiment, the present invention provides a computer programproduct for Sourcing and category management. The product includes acomputer readable storage medium readable by a processor and storinginstructions for execution by the processor for performing sourcing andcategory management method in supply chain.

In an advantageous aspect, the system and method of the presentinvention provides ability to overlay pieces of the structured data toarrive at actionable insights. Also, enables setup of automated ongoingsourcing and category management processes based on the nuanced categoryrequirements. Like, automatic curation of a souring demand through ademand aggregation algorithm, identification of appropriate baselinethrough price benchmarking, down listing suppliers & launching thesourcing event, automatic screening of responses, auto-identifying bestfit scenarios for the given event to recommend awarding decision andongoing monitoring of supplier and contract related risk through theworkbench. Further the system of the invention configures and receivesalerts on a periodic basis to review progress and make modifications orcourse corrections which help the system identify patterns specific tothe category and enhance the automation flow through machine learningsystems.

In an advantageous aspect, the present invention utilizes MachineLearning algorithms, prediction data models, recommendation algorithms,qualitative and quantitative data analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be better understood and when consideration is givento the drawings and the detailed description which follows. Suchdescription makes reference to the annexed drawings wherein:

FIG. 1 is a view of a Sourcing and category management system inaccordance with an embodiment of the invention.

FIG. 2 is a flowchart depicting a method of sourcing and categorymanagement in accordance with an embodiment of the invention.

FIG. 3A is a flow diagram depicting a method of supplier recommendationin an example scenario in accordance with an embodiment of theinvention.

FIG. 3B is a table showing supplier score and ranking for the examplescenario in accordance with an embodiment of the invention.

FIG. 3C is a system support architecture for a supplier recommendationin accordance with an embodiment of the invention.

FIG. 3D shows a table providing attributes obtained from different datasources in accordance with an embodiment of the invention.

FIG. 3E shows a table showing label taxonomy in accordance with anembodiment of the invention.

FIG. 3F shows an overview of preprocessing performed on datadescriptions in accordance with an embodiment of the invention.

FIG. 3G is a block diagram showing different components of a dataclassifier in accordance with an embodiment of the invention.

FIG. 3H is a skip-gram model of word embedding in accordance with anembodiment of the invention.

FIG. 3I is a block diagram showing concatenating words embedding withcharacter embeddings in accordance with an embodiment of the invention.

FIG. 3J is a flow diagram for supplier recommendation in examplescenario with line description in accordance with an embodiment of theinvention.

FIG. 3K is a table showing item to category mapping in accordance withan embodiment of the invention.

FIG. 4 is a flow diagram of a negotiation strategy executed by anauto-negotiator in accordance with an embodiment of the invention.

FIG. 5A shows a category workbench application user interface withactionable insights in accordance with an embodiment of the invention.

FIG. 5B shows a category workbench application user interface withdetails of spend profiles in accordance with an embodiment of theinvention.

FIG. 5C shows a category workbench application user interface withimpact of certain parameters for executing sourcing operation in supplychain in accordance with an embodiment of the invention.

FIG. 5D shows category workbench application user interface with insightinto cost drivers in accordance with an embodiment of the invention.

FIG. 5E shows category workbench application user interface withinsights into market indices in accordance with an embodiment of theinvention.

FIG. 5F shows category workbench application user interface withinsights into strategy in accordance with an embodiment of theinvention.

FIG. 5G shows category workbench application user interface withoverview of Projects in accordance with an embodiment of the invention.

FIG. 5H shows category workbench application user interface withinsights into spend and savings in accordance with an embodiment of theinvention.

DETAILED DESCRIPTION

Described herein are the various embodiments of the present invention,which includes method and system for sourcing and category management insupply chain.

The various embodiments including the example embodiments will now bedescribed more fully with reference to the accompanying drawings, inwhich the various embodiments of the invention are shown. The inventionmay, however, be embodied in different forms and should not be construedas limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. In the drawings, the sizes of components may beexaggerated for clarity.

It will be understood that when an element or layer is referred to asbeing “on,” “connected to,” or “coupled to” another element or layer, itcan be directly on, connected to, or coupled to the other element orlayer or intervening elements or layers that may be present. As usedherein, the term “and/or” includes any and all combinations of one ormore of the associated listed items.

Spatially relative terms, such as “constraints,” “analysis,” or “datalake,” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the structure in use or operation in addition to theorientation depicted in the figures.

The subject matter of various embodiments, as disclosed herein, isdescribed with specificity to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different features orcombinations of features similar to the ones described in this document,in conjunction with other technologies. Generally, the variousembodiments including the example embodiments relate to a system andmethod for autonomous sourcing and category management in enterpriseapplications.

Referring to FIG. 1 , a system 100 for sourcing and category managementis provided in accordance with an embodiment of the present invention.The system 100 includes at least one entity machine 101 with categoryworkbench user interface 101A for sending, receiving, modifying ortriggering processing of category-based sourcing data over a network102. The system includes a server 103 configured to receive data andinstructions from the entity. The system 100 includes a supportmechanism 104 for performing sourcing process with multiple functionsincluding contract data extraction, classification and structuring ofdata attributes for analysis of data, creation of data models configuredto process different parameters including supplier data, impact data,historical data etc. The system 100 includes a data lake 105 foraccessing item or service related data from entities and storingplurality of training classification models created by support mechanism105.

In an embodiment the server 103 of the invention may include varioussub-servers for communicating and processing data across the network.The sub-servers include but are not limited to content managementserver, application server, directory server, database server, mobileinformation server and real-time communication server.

In an embodiment the entity machine 101 may communicate with the server103 wirelessly through communication interface, which may includedigital signal processing circuitry. Also, the entity machine 101 may beimplemented in a number of different forms, for example, as asmartphone, computer, personal digital assistant, or other similardevices.

In an exemplary embodiment, the category workbench interface 101A of theentity machine 101 enables cognitive computing to improve interactionbetween user and the supply chain application(s). The Category workbenchinterface 101A improves the ability of a user to use the computermachine itself. Since, the category workbench 101A provides actionableinsights into various category of information including but not limitedto spend category, supplier regions spend, actual/vs target spend, topcost drivers, strategies, etc., at the same instant, the interfacethereby enables a user to take informed decision or undertake anappropriate strategy. The category workbench application interface 101Atriggers a plurality of predictive data models to identify one or morecategory of objects eligible for sourcing. By eliminating multiplelayers, processing tasks and recordation of information to get a desireddata or functionality, which would be slow, complex and impractical tolearn, particularly to a user without deep knowledge of the subject, thecategory workbench 101A is more user friendly and improves thefunctioning of the existing computer systems.

In an example embodiment, the support mechanism 104 of the system 100includes a control interface for accessing demand related informationreceived at the server 103. The support mechanism further includes asourcing module 107 triggered through the category workbench 101A of theentity machine 101 for initiating at least on task based on the receiveddemand at the server 103.

The support mechanism 104 includes a verification engine forverifying/identifying if the demand request is received from an entityor triggered by the system after completion of an application functionor auto generated. The mechanism 104 further includes a controller 108encoded with instructions, enabling the controller 108 to function as abot for autonomous sourcing and category management applicationoperations. The mechanism 104 also includes an object specific datamodel mechanism (OSDM) as part of the data model database within entityspecific data in the data lake 105. The object includes item or servicefor sourcing as a supply chain operation. The support mechanism 104includes an AI engine 109 configured for enabling generation of aplurality of data script depending on the multiple data models likeobject specific data model, supplier recommendation data model etc. Themechanism 104 includes data cleansing and classification engine 110 forcleansing of data and categorization of objects, a crawler 111 foridentifying relevant information from various sources including newsfeeds, contracts, supplier data on web etc., a data solver and optimizer112 for processing variables, bid optimization and recommend suppliers.The data solver and optimizer 112 is configured for identifyingconstraint associated with suppliers before processing, a processor 113configured for performing various functions including but not limited toselecting appropriate data attributes, identifying positioning of thedata attributes, processing object related data based on multiple datamodels for recommending supplier in a sourcing operation etc. The AIengine 109 is coupled to the processor 113 for prediction of constraintsand recommendation of supplier. The mechanism includes a data extractionand mapping module 114 configured for extracting and mapping object datato category and supplier by clustering script generated through the AIengine 109. The mechanism 104 includes an API 115 for triggeringmultiple data models through the processor 113 for carrying out thesourcing operation. Since supply chain operations include multiplefunctions within the sourcing operation like supplier recommendation,item categorization, demand sensing etc., the support mechanism 114includes sub-processors 116 for carrying out multiple taskssimultaneously. Further, the sourcing operation includes tasks likesupplier recommendation, bid optimization and negotiation withsuppliers. The mechanism 104 includes an auto-negotiator 117 coupled tothe AI engine 109 configured for negotiating with the supplier based ona negotiation script generated by the AI engine 109. The negotiator 117processes information about suppliers, pricing, budget, market and bids.The negotiation script drives conversations with suppliers fornegotiation.

In an exemplary embodiment, the AI engine 109 is coupled to thecontroller 108 encoded with instructions enabling the controller 108 tofunction as a bot for processing the sourcing request based on theparameters. The set of parameters include parameters that determine ifthe demand is a generated after expiry of a contract or a demanddirectly by the entity etc., The parameters may include entity name,existing contract details, pricing information, object procured, date ofprocurement, place, etc. It shall be understood to a person skilled inthe art that the parameters may vary depending on the request and sourceof request like from an entity or auto generated request from anapplication after completion of an operation of the application orgeneration of an auto-set demand trigger through the application.

In example embodiment the server 103 shall include electronic circuitryfor enabling execution of various steps by the processor. The electroniccircuitry has various elements including but not limited to a pluralityof arithmetic logic units (ALU) and floating-point Units (FPU's). TheALU enables processing of binary integers to assist in formation of atleast one table of data attributes where the OSDM and entity specificdata model (ESDM) or either similar data models are applied to the datatable for obtaining supplier score of recommending suppliers. In anexample embodiment the server electronic circuitry includes at least oneAthematic logic unit (ALU), floating point units (FPU), otherprocessors, memory, storage devices, high-speed interfaces connectedthrough buses for connecting to memory and high-speed expansion ports,and a low speed interface connecting to low speed bus and storagedevice. Each of the components of the electronic circuitry, areinterconnected using various busses, and may be mounted on a commonmotherboard or in other manners as appropriate. The processor 113 canprocess instructions for execution within the server 103, includinginstructions stored in the memory or on the storage devices to displaygraphical information for a GUI on an external input/output device, suchas display coupled to high speed interface. In other implementations,multiple processors and/or multiple busses may be used, as appropriate,along with multiple memories and types of memory. Also, multiple serversmay be connected, with each server providing portions of the necessaryoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

In an example embodiment, the system of the present invention includes afront-end web server communicatively coupled to at least one databaseserver, where the front-end web server is configured to process therecommended strategy based on a plurality of scripts by receiving therecommended strategy processed by the server and applying an AI baseddynamic processing logic to the strategy to automate at least one task.

The processor 113 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 113may provide coordination of the other components, such as controllinguser interfaces, applications run by devices, and wireless communicationby devices.

The Processor 113 may communicate with a user through control interfaceand display interface coupled to a display. The display may be, forexample, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or anOLED (Organic Light Emitting Diode) display, or other appropriatedisplay technology. The display interface may comprise appropriatecircuitry for driving the display to present graphical and otherinformation to an entity/user. The control interface may receivecommands from a user/category manager and convert them for submission tothe processor. In addition, an external interface may be provided incommunication with processor 113, so as to enable near areacommunication of device with other devices. External interface mayprovide, for example, for wired communication in some implementations,or for wireless communication in other implementations, and multipleinterfaces may also be used.

In an embodiment, the present invention uses GPUs (Graphical processingunits) for enabling AI to provide computing power to processes humongousamount of data.

In an exemplary embodiment, the Artificial intelligence engine 109employs machine learning techniques that learn patterns and generateinsights from the data. Further, the AI engine with ML employs deeplearning that utilizes artificial neural networks to mimic biologicalneural network in human brains. The artificial neural networks analyzedata to determine associations and provide meaning to unidentified data.

In another embodiment, the invention enables integration of ApplicationProgramming Interfaces (APIs) 115 for plugging aspects of AI into thesourcing application.

Referring to FIG. 1 , the various elements like the support mechanism104 and the data lake/memory data store 105 are shown as externalconnections to the server 103 in accordance with an embodiment of theinvention. However, it shall be apparent to a person skilled in the artthat these elements may be part to an integrated server system. Also,some of the sub-elements of the support mechanism 104 and the datalake/memory data store 105 either alone or in various combinations maybe part of a server system as other external connections.

In an example embodiment, the data lake/memory data store 105 includesplurality of databases as shown in FIG. 1 . The data store 105 includesa data model database 118 storing a plurality of data models relevant tothe data attribute of objects for extracting object data from historicaldatabase 119, the historical database 119 stores transaction dataincluding spend data, object data, contract data etc., from one or moreentities, a supplier database 120 configured for storing supplierrelated data, an operational database 121 configured for storing a setof parameters identified from a received demand for initiating asourcing request. The data model database includes an entity specificdata model (ESDM) database, an object specific data model (OSDM)database and a data script database (DSD) configured for storing aplurality of data script generated by the AI engine based on analysis ofthe recommended sourcing strategy. The data script is generated based onprediction analysis, and deep learning performed on historical database119 and entity specific historical database. The data script includes aset of queries processed by dynamically generated AI based processinglogic. The data lake 105 further includes a plurality of registers 122as part of the memory data store 105 for temporarily storing data fromvarious databases to enable transfer of data by a processor between thedatabases as per the instructions of the AI engine 109 to create astrategy. Further, the data model database 118 is configured for storinga plurality of training data models required to fetch data attributesfor creating a questionnaire based on sourcing request and identifiedstrategy. The data lake 105 includes a graph database 123 configured forstoring graphical data model where multiple criterion such as entityline of business and region of suppliers can also be used as additionalfilters to recommend the best possible list of suppliers. The data lake105 includes a constraint database 124 configured for storing implicitand explicit constraints utilized for determining supplier score forrecommending a supplier. The data lake also stores key performanceindicator (KPI) information about suppliers based on information in thehistorical database 119 related to past contracts, execution andcompliance with legal obligations under the contracts etc. The data lake105 also includes an impact parameter database 125 storing real timeupdated information related to parameters impacting a sourcing decisionor supplier recommendation.

In an embodiment, the system retrieves one or more recommended suppliersfrom the supplier database 120 based on a plurality of factors includingfinancial performance and risk ratings, revenue details, financialstability, spend data, client servicing, logistics, lead times, marketfragmentation, capacity constraints, certifications, incumbent status,currency fluctuations and political risks.

In an embodiment, the processing logic for identifying a recommendedstrategy or a recommended supplier or a negotiation strategy, issequential or parallel or switching based processing of the dataattributes for generating the data script to ensure faster processing ofthe request. The switching-based processing logic includes dynamicidentification of a path for processing of the request based on the datascript and determination of multiple data attributes dependent on eachother.

In an embodiment, the recommended strategy is determined based on datapoints including evaluation of operational objectives, total Cost ofOwnership and lifecycle, engagement and pricing models, compliancelevels, analysis of historical policies and strategies, consumptionpatterns, behaviour and performance data, opportunities forconsolidation of volumes across geographies, business units, product andservice categories, volume tier discounts, new technologies, substituteproducts, low cost alternatives, standardization or reuse opportunities,benchmarks for resource qualifications and experience, intervals forprice negotiations, futures, forwards, and options to fix or cap pricesof commodity purchases in liquid markets, currency hedging for materialswhich are predominantly imported, Value chain for opportunities forVertical integration, Should cost by leveraging data model to negotiateon billing rates, material and equipment price, supplier mark-up/profit,and current inventory management practices.

In an embodiment, the object specific data model (OSDM) is generated byanalyzing the plurality of object data from the historical database 119where the database 119 includes the plurality of object data extractedafter optical character recognition of past executed contracts by one ormore entities. The system further analyzes historical data through theworkbench application interface and perform AI based budget predictionsand demand aggregation by overlaying a historical spend data withdisparate forecasting models built on various data sources available toanalyze spend and pricing trends for the object.

The AI engine 109 predicts and recommends suppliers by processingsupplier information in the supplier database 120 and past contractrelated information in historical database 119 to provide supplier scoreand ranking. The system 100 also includes an authentication mechanism toensure each recommended supplier is validated automatically, therebysaving time and increasing security.

The memory data store 105 may be a volatile, a non-volatile memory ormemory may also be another form of computer-readable medium, such as amagnetic or optical disk.

The memory store 105 may also include storage device capable ofproviding mass storage. In one implementation, the storage device may beor contain a computer-readable medium, such as a floppy disk device, ahard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations.

The computing devices referred to as the entity machine, server,processor etc. of the present invention are intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, and other appropriatecomputers. Computing device of the present invention further intend torepresent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smartphones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be exemplary only, andare not meant to limit implementations of the inventions describedand/or claimed in this disclosure.

In an embodiment, the system is provided in a cloud or cloud-basedcomputing environment. The autonomous sourcing and category managementsystem enables more secured process considering the issues inherent withcloud environments.

In an embodiment, the entity or user includes a client, a categorymanager, one or more operation of an Enterprise applicationautomatically generating the sourcing demand request based on executionof the operation like expiry of an existing contract, direct sourcingrequest or occurrence of any such operation triggering the applicationto generate the demand or request.

In an exemplary embodiment, the autonomous sourcing and categorymanagement system 100 of the present invention is configured foranalyzing impact of a plurality of varying parameters (changes inpricing, supply demand) on sourcing decisions factors to predict thesourcing strategy. The varying parameters include market dynamics andinternal spend drivers across suppliers, parts, products, commodities,and business units/plants across various Regions. The inventionevaluates leading indicators in the market applicable for the Categoryin conjunction with correlated factors to provide predictions on changesin material costs, product margins, supply constraints, supplierfinancial risk, etc. to enable proactive procurement decisions. Thesedecisions will avoid cost pressures, minimize new risks, or lock-insavings to eventually recommend strategies to be executed through thecategory workbench 101A.

Referring to FIG. 2 , a flowchart 200 depicting a method of sourcing andcategory management is provided in accordance with an embodiment of thepresent invention. The method includes the steps of 201 receive a demandfrom a data source such as a plant maintenance team, through expiredcontracts etc. at a server. In step 202 a category workbench receivesinformation related to the demand and in 203 a sourcing module istriggered on the category workbench application interface for initiatingat least one task. In step 204 the system checks price trends and shouldcost models for analysis. In step 205, a target price is added to theworkbench for negotiation. In step 206, existing or expired contracts inthe data lake are analyzed. In step 207, if no contracts are found thenrecommending new strategy to be added to the workbench. In step 208, ifmultiple contracts are found then, contract consolidation strategy isadded to the workbench. In step 209, checking if existing prices areless than or equal to target prices. In step 210, determining a need foran RFP if the existing prices are more than target prices. In step 211,determining baseline using embedded rate guide and defining savingtargets. In step 212, recommending suppliers for the RFP on performanceratings, past savings and lead times. In step 213 approving or addingsupplier through workbench, and in 214 generating questionnaire andprice sheets specific to the category, industry and market landscape.For executing the method, AI engine coupled to the controller enablesthe controller to function as a bot to create Price sheet for bids withtarget price for each object, identifies and inject implicit or explicitconstraints such as constraint on Volume, Delivery, region etc. The botalso creates a questionnaire for object categories and recommendssuppliers through processing of historical data, impact parameters andresponse received to the questionnaire. The AI engine enables creationof data scripts for processing multiple tasks including generation ofrelevant questionnaire, supplier recommendation, optimization, andobject characteristic data set. In step 215 floating the RFP to selectedsuppliers and receiving supplier response in 216. In step 217,generating constraints and scenarios for bid analysis. In step 218,triggering a bot to analyze bids, where in case of high variance,providing feedback to suppliers to lower prices based on target pricesand supplier responses in 219. In step 220, recommending strategy ofreverse auction or negotiation in case of low variance i.e the bids areclose. In step 221, recommending negotiation strategy and generatingnegotiation script. In step 222, analyzing new bids to recommend awarddecisions and in step 223, calculating savings. In step 210, if it isdetermined that the existing prices are less than or equal to the targetprice then in step 224 approving the pricing through category workbench.In step 225, communicating the award decision.

If the autonomous sourcing method and system determines significant costsaving then with reference to steps 211-214 above, a bot creates objectcharacteristic data sets such as price sheets for RFX (bids) with targetprices for each item, constraints such as volume, delivery, regionsetc., and generates questionnaire by AI based processing of item/servicecategories. Depending on the number of suppliers, a decision on biddingor creation of RFX is taken. For E.g., If number of participatingsuppliers is less than 4 and auction is triggered, else a sourcing eventin RFX is initiated. The participating Suppliers respond to thequestionnaire and price sheet information, and a dynamicfeedback/bidding process is initiated. As part of the bidding process,once the target prices are met and the auto bid optimization recommendsallocation of item/services across different suppliers an auto generatedconsent terms and conditions is triggered and sent the suppliers foracceptance.

In an embodiment, the invention examines the bids against the targetprices and average. It gives feed to suppliers on prices in terms oftheir position and variance from the average and lowest bids.

In an example embodiment, the at least task includes processing shouldcost models, trends of raw materials, price benchmark of objects etc.,for providing actionable insights on the application interface.

In another example embodiment, the recommended strategy includes, autorenewal of existing contract, initiation of sourcing event with RFx,auction or bids etc. These strategies are based on the actionableinsights. It is understood to a person skilled in the art that RFxrefers to documents including but not limited to request for Proposal(RFP), request for information (RFI), request for Quote (RFQ) etc.

In an embodiment the identification of suppliers for awarding thesourcing contract includes evaluation of parameters associated with atleast one of supplier management operations, procurement operations,inventory management operations, account payable operations,transportation management operations, and material managementoperations.

In an embodiment, the data sources include expiring contracts, blanketpay orders, should cost models built on market indices and prices,transactional spend data, demand planning, ERPs, budgets, supplyplanning, newsfeeds, Merger and Acquisition information, bankruptcy,innovation and spin-off.

In an example embodiment, the demand is sensed through a plurality ofdata sources like the system triggers order for object but determinesall quantity against the contract has been used. The system identifiesthat inventory level for the object is about to reach safety sock andthe plant needs to place and order quickly to account for the lead time.The demand may also be generated if an organization is opening a newplant as a part of the expansion plan. Also, depending on the marketconditions the system may predict and increase in demand for an item,therefore, organization needs to produce more by utilizing more capacityof existing plants. since more lines will be operational, more inventoryis needed.

In an example embodiment, for determining a target price the systemafter checking with price benchmarks and should cost models, determinesthat the DDP (Delivered Duty Paid) price for the object should be 0.50USD/lbs-0.65 USD/lbs.

In an example embodiment, when the system determines there are no activecontracts for an object/item, it recommends a new sourcing strategy likecombination of low-cost country sourcing and buying recycled object etc.

In another example embodiment, from the current prices paid and volumes,the system determines a baseline price of 0.72 USD/lbs, annual volume of3,000 tonnes, and baseline spend of USD 4.76 MM. therefore, the expectedsavings are: USD 0.46-1.46 MM.

In an example embodiment, as part of the recommended strategy, thesystem analyzes multiple contracts with different suppliers ofitem/object for a particular plant/location. The system adds this to thelist of criteria for recommending sourcing strategy. In some of thecontracts, the price is within the system recommended range and thesystems recommends through the category workbench to consolidate supplywith these suppliers and connect with them.

In an embodiment, after understanding the complexity of the category andsupply chain complexities associated with say sourcing from low costcountries (e.g., long lead times) and risks and standards for recycledmaterials, the system auto-generates a questionnaire through a bot. TheAI engine processes historical and standard RFPs for the category andthe should cost models used depending on real-time changing marketdynamics, to generate an object characteristic data set. The inventionincludes a historical query knowledge database configured for storing aplurality of questions processed by the AI engine based on a pluralityof parameters and the object characteristic data set to generate aquestionnaire where one or more suppliers are identified based on aresponse to the questionnaire received from one or more recommendedsupplier.

The historical query knowledge database is a question bank with afeedback loop. The AI engine keeps learning from the new questionnairecreated every time and stores the questions in the bank. Depending onthe demand and strategy the questionnaire is auto populated and sent toone or more suppliers for receiving a response. The set of questionsfetched from the question bank are based on the requirement and may beinfluenced by certain impact parameters. The AI engine fetches thequestions from the bank to create the questionnaire are based on ascript generated by the bot for processing the requirement.

In an embodiment, as part of the negotiation strategy the systemgenerates a script to further negotiate with the suppliers, e.g.,falling prices of crude, additional volume discounts from volumeconsolidation, sign on bonus for incumbents, pricing formula to use,best payment terms etc. The negotiation script is generated based on oneor more negotiation data models trained through natural languageprocessing (NLP) of a historical dataset with logistic regression andmedian calculations to predict recommendations. Further, therecommendations predict one or more optimum negotiation approach andmost effective negotiation parameters.

In an exemplary embodiment the invention includes auto contract creationwith legal and commercial terms and conditions based on the selectedawarding scenario after negotiation. Further, the invention includesrisk assessment associated with source to contract at various stages ofthe processes.

The system is configured to determine an objective function forselecting suppliers to minimize spend. The system automaticallygenerates award scenarios, such as lowest bid, max 2 suppliers for eachplant, 2 suppliers across all the plants, incumbent supplier scenarios,scenario with optimum suppliers etc. The system through categoryworkbench application recommends the best scenario for a user. In analternate embodiment, a user can create a new scenario with newconstraints.

In an embodiment, the present invention provides the autonomous sourcingmethod that includes bid optimization through analysis of constraints.In procurement industry, a buyer would like to procure a number of itemsacross multiple suppliers who are willing to furnish them at differentprices (bid price). The objective is to meet this demand, targetquantity (number of units), for each such item across multiple supplierswith cheapest possible cost. However, not every supplier would befurnishing all the items required by the buyer, hence there will be aneed to work with multiple suppliers. Further, there may be cases wherea single supplier cannot guarantee to furnish the target quantity unitsfor an item all by themselves, which is called as supplier max capacityfor that item. All such real-world scenarios and conditions make theoptimization problem very challenging to model and solve. In view of thesame, the optimization method of the present invention includes solvervariables that are determined by the data solver, some of them beingintermediate variables and some output variables. The auto optimizationby the system through a data solver and optimizer includes operatingwith mixed Integer and Non-Integer scripts to accomplish minimizing costor maximizing savings and constraints.

In an example embodiment, the data solver and optimizer utilize thebelow variables:

-   -   (1) Set of unique bids in the price sheet (B): Bid set    -   (2) Set of unique suppliers with eligible bids in price sheet        (S): Supplier set    -   (3) Set of unique items in price sheet (I): Item set    -   (4) Cost of item per unit (C0): Coefficient 0    -   (5) Freight or other cost independent of number of units of item        (C1): Coefficient 1    -   (6) Target quantity of an item. Non-negative Integer (TQ):        Target Quantity    -   (7) The Internal Variables includes determination of Bid being        selected or not. This variable is needed as the cost function        has a freight cost term (BBV): Bid Boolean Var    -   (8) Number of units supplied by supplier across all items.        Non-negative Integer (SQV): Supplier Quantity Var. If a supplier        is selected or not for solution (SBV): Supplier Boolean Var    -   (9) The Output Variables include Quantity allocated to a bid.        Non-negative Integer (QV): Quantity Var.

Vector Lengths:

-   -   BBV=|B|    -   QV=|B|    -   SQV=S|    -   TQ=|I|    -   SBV=|S|    -   And cost function of a bid, uniquely identified by (item,        supplier, cost) triplet by a supplier is given by

cost_(b) =C0_(b)*QV_(b) +C1_(b)*BBV_(b)  (1)

-   -   The first term is variable cost which is proportional to number        of units allocated while the second term is fixed cost which is        independent of number of units. The bid Boolean flag (either 0        or 1) makes sure of such fixed cost is only counted only once        towards total cost if the bid is selected towards allocation.    -   The optimization includes minimizing the following cost function        f(x) which is the summation of above expression 1 for all the        bids submitted to the bidding process.

$\begin{matrix}{{f(x)} = {\sum\limits_{b = 1}^{❘B❘}\left( {{C0_{b}*{QV}_{b}} + {C1_{b}*{BBV}_{b}}} \right)}} & (2)\end{matrix}$

In an exemplary embodiment, the invention processes Constraints that area set of conditions frequently requested by the procurement industrytowards allocation of units to suppliers. These constraints are based onattributes of suppliers or items or bids. For example, preferredSuppliers vs non-preferred suppliers, incumbent vs non-incumbentsuppliers, supplier categories based on other attributes, itemcategories, etc. are a few to name. The constrains are Implicit orExplicit constraints. Some of the other e.g., includes diversity status,contractual commitment of volumes/spend, and minimum number of suppliersto address risks.

The Implicit constraints are the set of constraints that are not createdby the end user (from front end), but rather by the optimizerimplicitly. These set of constraints are a mandatory set of conditionsthat must be satisfied towards the allocation of units to bids. Theimplicit constraints include target Quantity constraint, SupplierBoolean constraint, Bid Boolean constraint and Supplier quantity Varconstraint.

Target Quantity Constraint

-   -   For every item the target quantity must be satisfied across        suppliers' allocation for that item. There are I such        constraints, one per item.

$\begin{matrix}{{TQ}_{i} = {\overset{❘B_{i}❘}{\sum\limits_{b = 1}}{\left( {QV}_{b} \right){\forall{i \in {IB_{i}} \in B}}}}} & (3)\end{matrix}$

-   -   where B_(i)=Bids belonging to Item i across all suppliers.

Supplier Boolean Constraint

-   -   This variable is used to control the number of suppliers being        selected for allocation for all items. It identifies the        supplier who is supplying at least one unit of any item across        all items. Sometimes there is a requirement to limit the number        of suppliers the buyer wants to work with in the procurement        process. This variable is created one for each unique supplier.

${SB_{\overset{˙}{b}}} = \left\{ {\begin{matrix}0 & {0 \leq {SQV}_{s} \leq 0.9} \\1 & {0.9 \leq {SQV}_{s} \leq \infty}\end{matrix}{\forall{s \in S}}} \right.$

Bid Boolean Constraint

-   -   If quantity allocation for a bid is greater than 0 then bid        Boolean is 1 for that bid or else zero. There are B such        constraints. The fixed cost of a bid is added to the total        procurement cost if the allocated quantity is at least one unit        for that bid.

${BBV}_{b} = \left\{ {\begin{matrix}0 & {0 \leq {QV}_{b} \leq 0.9} \\1 & {0.9 \leq {QV}_{b} \leq \infty}\end{matrix}{\forall{b \in B}}} \right.$

Supplier Quantity Var Constraint

-   -   Sum of quantities across all items for each supplier. There will        be |SI such constraints; one for every supplier.    -   This constraint is needed create supplier Boolean constraint

$\begin{matrix}{{SQV}_{j} = {\sum\limits_{i = 1}^{|B_{s}|}{\left( {QV}_{i} \right){\forall{j \in {SB_{s}} \in B}}}}} & (4)\end{matrix}$

-   -   where B_(s)=Bids belonging to supplier s across all items.    -   The Explicit Constraints are the set of constraints created by        end user using product front end towards scenario creation.        These are converted to mathematical expressions accordingly.

Supplier Subset Allocation Constraint

-   -   For a given subset items SI allocation happens only from subset        bids SB of Subset Suppliers (SS) and not from all suppliers. Now        the conditions:

$\begin{matrix}{{QV}_{c} = {0{\forall{c \in {CSB}}}}} & (5)\end{matrix}$ $\begin{matrix}{{\sum\limits^{SBS}{QV}_{ij}} = {{SQV}_{j}{\forall{i \in {S{Bj}} \in {SSB}}}}} & (6)\end{matrix}$ $\begin{matrix}{{low} \leq {\sum\limits_{j}^{SS}({SBV})} \leq {high}} & (7)\end{matrix}$

-   -   Let all the bids from the complementary subset suppliers for the        same item subset SI be called complementary subset bids CSB. The        above sentence implicitly states that the quantity allocated to        CSB is zero defined by condition (5).    -   Condition (7) is only set when either lower bound and/or upper        bound is given. These bounds are applied and only a subset        number of suppliers is selected from the given (SS) accordingly.

$\begin{matrix}{{TQ}_{p} = {\underset{j = 1}{\sum\limits^{B_{p}}}{\left( {QV}_{b} \right){\forall{p \in {{SI}B_{p}} \in {SB}}}}}} & (8)\end{matrix}$

-   -   The condition (8) overridden and replaces the condition (3) for        all items in (SI)

Supplier Advantage Disadvantage by Percentage

-   -   This condition is to provide either advantage or disadvantage to        supplier across subset items by either decreasing or increasing        respectively the coefficient 0 and coefficient 1 values in bids        by a percentage value. This is a pre-processing step that        manipulates the data rather than adding a mathematical        constraint to the solver.

Supplier Advantage Disadvantage by Value

-   -   This condition is to provide either advantage or disadvantage to        supplier across subset items by either decreasing or increasing        respectively the coefficient 0 and coefficient 1 values in bids        by value. This is a pre-processing step that manipulates the        data rather than adding a mathematical constraint to the solver.        The true advantage/disadvantage value is only visible if the        supplier is allocated the total value he bid for. Otherwise it        is simply proportionate advantage/disadvantage.

Range Allocation

-   -   At times the Supplier Subset allocation constraint allocates an        impractical allocation like for example: in allocating 100 units        of target quantity 99 units are allocated to supplier ‘A’ and        only 1 unit to supplier ‘B’, mathematically this might be        correct, but very impractical. For a given subset bid ids, group        by bids on supplier ids, and avoid allocation between 0-lower        bound and between upper bound and max allocation. (only allow        allocation between lower bound, upper bound) To avoid such        situations the following two constraints are written down, one        by value percentage and other by number of units (volume) where        there is lower and/or upper bound to each allocated supplier.        Hence, either the supplier is allocated zero units/zero        percentage value, or something between the ranges mentioned in        this constraint.    -   Range Allocation Value Percentage

$\begin{matrix}{D = {\sum\limits_{b = 1}^{B}\left( {{C0_{b}*{QV}_{b}} + {C1_{b}*{BBV}_{b}{\forall{b \in {SB}}}}} \right.}} & (9)\end{matrix}$

-   -   where SB is subset bids of dataset.

C _(b) =C0_(b)*QV_(b) +C1_(b)*BBV_(b) ∀b∈SB  (10)

-   -   where SB is subset bids of dataset, and there will be |SB| such        constraints added to the problem.

BBV_(b)*(low*D−C_(b))<=0∀b∈SB  (11)

-   -   where SB is subset bids of dataset, and there will be |SB| such        constraints added to the problem.

C _(b)−high*D<=0 ∀b∈SB  (12)

-   -   where low and high are lower and upper bounds in percentage        allocation of value, and SB is subset bids of dataset, and there        will be |SB| such constraints added to such. Range allocation        volume

BBV_(b)*(low−QV_(b))<=0))∀b∈SB  (13)

-   -   where SB is subset bids of dataset, and there will be |SB| such        constraints added to the problem.

QV_(b)−high<=0∀b∈SB  (14)

-   -   where low and high are lower and upper bound of allocation of        number of units for every bid and SB is subset bids of dataset,        and there will be |SB| such constraints added to the problem.

In an embodiment, the method of autonomous sourcing includes supplierrecommendation. The supplier recommendations are generated by theautomated systems through the bot and AI engine. When a user wantssupplier recommendations at a category, region, business unit level, theSystem first auto-generates a price sheet with target prices,constraints (region of supplier, min volumes etc.,). The price sheet mayhave several line item descriptions that are textual. Since, textualdescriptions are difficult to understand/comprehend and there are nodirect mappings between items and suppliers that are maintained in ERPsystems or databases, there are no relations between items and thecategories.

In an exemplary embodiment, the present invention collates and scrubsdata from one or more internal and external databases including ERPs,Vendor Management Systems, Newsfeeds from top Industry Sources, MarketIndices, Demand Management and Inventory Management Systems for dataanalysis to predict spend.

The AI engine is configured for processing data based on plurality ofattributes/criteria including but not limited to Spend data acrossdifferent organizations for each category, Total spend for each categoryfor each organization, Number of organizations served by the supplier,Coverage: Number of items in each category catered for each supplier,Firmographic attributes: Total Revenue, profits, number of employees,regions operating in, contact information (emails, Phone number, Keypeople), diversity status etc., Supplier Rating as rated byorganizations in that industry, Activity like Number of bids/auctionsinvited, awarded to, Supplier Risk rating and number of transactionsetc.

Referring to FIG. 3A a flow diagram 300A for supplier recommendation inan example scenario is provided in accordance with an embodiment of theinvention. In the example scenario for Category, Level, Region thecategory of BUSINESS TRAVEL and Level1 is depicted in the diagram 300A.

-   -   In such a scenario, a supplier Score (S) is determined as:    -   Score for a Supplier S=W₁·X₁+W2X2+W3·X3+W4·X4+W5·X5+ . . . +WnXn        i.e.

${Si} = {\sum\limits_{i}^{n}{\sum{WiXi}}}$

-   -   Where Si is the score of the supplier, Wi=Weights of the        supplier on the attribute Xi, Xi is the attributes or the        criteria.    -   Xi is normalized using a scaler.

${Zi} = \frac{{Xi} - {\mu i}}{\sigma i}$

-   -   Where μi is the normal central tendency (Mean/Average in this        case) and σi is the standard deviation of the distribution of        that attribute.    -   The sum of all Weights Σ_(i=1) ^(n) Wi=1 and 0<=Wi<=1.0    -   The weights Wi are initially assigned to be equally distributed        unless stated in the RFx application.    -   The weights specified through the user interface are fetched by        the AI engine.    -   On user Feedback, the overall supplier score is given either a        reward or a penalty based on whether the supplier has been        selected for a next round.    -   Score_(new)=Score_(old)+Δ where Δ is a penalty or a reward,        depending on the selection. This reward weight penalizes it to        the extent that it ranks below the suppliers selected by the        autonomous sourcing application. The Supplier score and ranking        is shown in table 300B in FIG. 3B.

Referring to FIG. 3C a system support architecture for a supplierrecommendation is shown in accordance with an embodiment of theinvention. The support architecture 300C includes category workbenchuser interface 301, API 302 for attribute and element extraction, No SQLsearch engine 303, No SQL Database 304 and the Data lake 305. The Datalake 305 provides a feedback on the category workbench 301 forrecommending supplier. The data lake 305 receives information related tosupplier attributes from multiple data sources, crawled data from webrelated to supplier profile or newsfeed, questionnaire and outcomes andhistorical spend data. The data from the data lake 305 is fetched andprocessed by the Processor based on a script created by the AI engine.The architecture includes a data store 306 providing data to the No SQLDB 304 and a machine learning ML Layer 307 above the data lake forprocessing the data. The AI engine coupled to the processor encoded withinstructions enabling the processor to function as a bot for processingthe data in the data lake by generating a code related to the script forsupplier recommendation. Further, the system of the invention providesan AI based clustering script for mapping object (Item/Service) toSuppliers and Categories. The clustering script enables unsupervisedobject discovery. The support architecture for supplier recommendationincludes fetching information from executed contracts. The architecturemay further include convolutional neural network (CNN), data structuringblock for data extraction and training models to perform text cleansing,tokenization, vectorization, string classification, NER extraction etc.The architecture enables performance of read/write extracted dataattributes and data elements from the contract for training models andfetching pricing information and other required information associatedwith a supplier from contracts through the SCM application.

In an example embodiment, the system of the invention analyzes spenddata patterns of an entity through classification of the spend data intoa hierarchical taxonomy that provides insights into the spend patterns.The AI engine of the invention generates clusters from spend data acrossmultiple entities to discover common items or services that are procuredacross multiple entities and suppliers. The AI engine processes thespend data through an unsupervised and nonparametric clustering approachas the number of possible clusters are unknown. The spend data isobtained from several data sources. Each data sample has severalattributes obtained from different sources. These attributes are thesupplier name, PO description, GL description, invoice description andmaterials descriptions as shown in Table 300D of FIG. 3D. The AI engineprocesses a concatenated description as input to the data model for itemdiscovery. The spend data of multiple entities is consolidated into aunified hierarchical Spend taxonomy. This taxonomy has multiple levelsand the granularity increases with each level. Examples of the labeltaxonomy are shown in Table 300E of FIG. 3E.

In another embodiment, several preprocessing steps are performed toclean and enrich the descriptions as the descriptions can be noisy andreduce the performance of a data classifier like a spend dataclassifier. Referring to FIG. 3F, an overview of the preprocessingperformed on spend data descriptions is shown in block diagram 300F. Thesystem caters to entities from all over the world and the descriptionsmay be in any language other than English, and translation to English isapplied as a preliminary preprocessing step. There could be someinstances where detailed natural text descriptions are provided. Tohandle these samples, a pipeline of preprocessing is performed wherefrequently occurring words such as “the, an” are removed and wordlemmatization is performed which results in a description like othersamples.

In an exemplary embodiment, a convolution neural network is used forclassification that focuses on presence of keywords rather than sequencefor feature extraction as spend description is a short text containing aseries of keywords without grammatical structure. One-dimensionalconvolutions are performed on the sequence of word embedding vectorsprovided as inputs. Each convolution operation is referred to as afilter h and has a filter width w. The one-dimensional convolutionoperation for a word sequence f is given by:

${\left( {f \star h} \right)(m)} = {\sum\limits_{i = 0}^{w}{{h\lbrack i\rbrack}{f\left\lbrack {m - i} \right\rbrack}}}$

Three different window widths with plurality of filters (eg: 128filters) for each window width are used. This ensures filters learn fordifferent n-grams in a training dataset. The model output is a Softmaxlayer with a size equal to the number of categories present. The blockdiagram providing different components of the data classifier are shownin FIG. 3G. The categorical cross entropy (CE) loss function is used forcomputing the gradients for training the network.

Referring to FIG. 3H, provides a skip-gram model 300H of word embeddingsin accordance with an example embodiment of the invention. The semanticrelationship of words is encoded in the embedding space in the form ofsimilar vectors. The word embeddings are trained using the skip grammodel where the current word embedding is used to predict the wordembedding of the surrounding context. The extraction of Words isfollowed by several downstream tasks that rely on the use of NaturalLanguage Processing (NLP) with deep learning algorithms. A word or tokenis an atomic unit in text processing, and it is mapped to a featurespace that captures its semantic and syntactic meaning. This achieved bytraining word embeddings where each word is mapped to a vector ofdimension D. This D-dimensional embedding space captures therelationship between different words in the vocabulary. The wordembeddings are trained using the fast text framework that relies on theskip-gram model 300H. The word embedding of a token is mapped through atransformation to predict the word embedding vectors of its surroundingtokens:

= ∑ n = 0 N ∑ c ∈ C n log ⁢ ( p ⁡ ( w c ⁢ ❘ "\[LeftBracketingBar]" w n ) )

-   -   where L_(skip-gram) is the loss function used to train the word        embeddings, context C_(n) with words w_(c) is the set of indices        for words surrounding the target word wn. Fast-text also models        each word by using character n-grams. For the training of        embeddings all n-grams are extracted for n>=3 and n<6. Each        n-gram is associated with a vector u_(g), leading to the        following scoring function is:

${s\left( {w,C_{w}} \right)} = {\sum\limits_{g \in G_{w}}{u_{g}^{T}v_{C_{w}}}}$

-   -   where G, with n-grams g are the set of n-grams per word w, C_(w)        is the context for the word w. This is crucial to capture the        subtle differences between words having either the same suffix        or prefix. The vocabulary and training corpus for word        embeddings is obtained by using the text present in the        historical database. This is to ensure that the word embedding        space is specific to object text i.e. it captures the        grammatical structure and semantic meaning of words, sentences        present in characteristic description of the object.

Typical spend data descriptions can include several numeric attributesand industry specific keywords. These are encoded as out-of-vocabulary(OOV) w.r.t the word embeddings. However, such attributes could containuseful information for classification. For example, the numericattribute of “16-inch” cannot belong to the Travel-expense category.Character embeddings are used to represent such OOV words where eachcharacter in the word is represented by a Dc dimensional vector and thevectors of all characters in a word are aggregated using a characterlevel convolutional neural network (CNN). A block diagram 300I ofconcatenating word embeddings with character embeddings is shown in FIG.3I. This aggregated character embedding v_(c) is concatenated with theword embedding v_(w), to represent in each word in the text description.

v[v _(w) ,v _(c)]

In an exemplary embodiment, the unsupervised clustering of spend data isperformed using the CNN models described earlier. The present inventiontrains a data classifier on each level 1 label with the normalizedtaxonomy used as the output in a supervised learning setting. This dataclassifier is also used as a feature encoder as the feature spacelearned by this classifier at the prefinal layer captures a separablespace across items. To perform item discovery, all the data belonging toa level 1 label is encoded through its corresponding level 1 classifier.The resultant feature vectors capture the semantic meaning of the lineitem description.

In another exemplary embodiment, the present invention provides anonparametric clustering method i.e. database scan (DB Scan) to be usedon the feature vectors. Non-parametric clustering approach is used asthe number of items that could be present can be unknown beforehand andit can also be a large number. The DB Scan is applied in a hierarchicalfashion where the hypermeters for the algorithm are recursively tuneduntil all the clusters detected in the data are less than hundredline-items. The clustering approach is applied to the data per level 4label (FIG. 3I). The level 4 label is most granular representation ofcategorization available through the data taxonomy. Further granularitycan be achieved through the clustering approach. For example, clusteringon a level 4 label of “Meals” could result in “Sandwiches”, “BuffetMeals”, “Doughnuts” etc.

Each extracted cluster contains a list of line items which could belongto multiple entities, multiple suppliers and forming a list ofdescriptions. A representative name for the cluster is obtained for thecluster by finding the most common subsequence of words in the list ofdescriptions. For items belonging to level 1 labels such as MRO(Maintenance, Repair and Operations), items could have multiplealpha-numeric attributes. A list of all possible alpha-numericattributes are extracted from the detected cluster. For example, acluster such as “Nuts” could have numeric attributes of “1 mm”, “2 in”,“3 in”. A list of unique suppliers and entities that belong to thiscluster are also collated. This meta-data for all extracted clusterscould have several use cases such as Master Data Management, SupplierRecommendation etc.

One such potential application is supplier recommendation. For an entitythat would like to procure a certain item, the item can be queriedacross all the detected clusters and the list of suppliers for the bestmatch cluster could be retrieved. Further, these clusters could berepresented in the form of a graph database where multiple criterionsuch as entity line of business and region of suppliers can also be usedas additional filters to recommend the best possible list of suppliers.

Referring to FIG. 3J, flow diagram 300J for supplier recommendation inanother example scenario is provided in accordance with an embodiment ofthe invention. In the example scenario, a Line description in pricesheets is provided where a search by the AI engine is performed for veryquick and accurate results. For eg: “½ pipe nipple valves”, Region.

In an example embodiment the system provides supplier recommendation as{“Recommended_Suppliers”: [{“ContactEmail”: “None”, “SupplierCategory”:“½ pipe nipple valves”, “SupplierCategoryAIL1”: “MRO”,“SupplierCategoryAIL2”: “MRO SUPPLIES”, “SupplierCategoryAIL3”: “PIPES,VALVES & FITTINGS”, “SupplierCategoryAIL4”: “PIPE AND PIPE FITTINGS”,“SupplierCountry”: “UNITED STATES OF AMERICA”, “SupplierName”: “ABCINDUSTRIES INC”, “SupplierPartnerCode”: “None”,“SupplierPreferredOrNot”: null, “SupplierRank”: “1”, “SupplierRegion”:“AMERICAS”}, {“ContactEmail”: “None”, “SupplierCategory”: “½ pipe nipplevalves”, “SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MROSUPPLIES”, “SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “PIPE AND PIPE FITTINGS”, “SupplierCountry”:“UNITED STATES OF AMERICA”, “SupplierName”: “XYZ AUTO PARTS”,“SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “2”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “PIPE AND PIPE FITTINGS”, “SupplierCountry”:“UNITED STATES OF AMERICA”, “SupplierName”: “WXY INDUSTRIAL TECHNOLOGIESINC”, “SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “3”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “PIPE AND PIPE FITTINGS”, “SupplierCountry”:“UNITED STATES OF AMERICA”, “SupplierName”: “XYZ CORP”,“SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “4”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “PIPE AND PIPE FITTINGS”, “SupplierCountry”:“UNITED STATES OF AMERICA”, “SupplierName”: “ABC BATES CO”,“SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “5”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “CONTROL VALVES”, “SupplierCountry”: “UNITEDSTATES OF AMERICA”, “SupplierName”: “XYZ INC”, “SupplierPartnerCode”:“None”, “SupplierPreferredOrNot”: null, “SupplierRank”: “6”,“SupplierRegion”: “AMERICAS”}, {“ContactEmail”: “None”,“SupplierCategory”: “½ pipe nipple valves”, “SupplierCategoryAIL1”:“MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”, “SupplierCategoryAIL3”:“PIPES, VALVES & FITTINGS”, “SupplierCategoryAIL4”: “CONTROL VALVES”,“SupplierCountry”: “UNITED STATES OF AMERICA”, “SupplierName”: “ABCPERFORMANCE INC”, “SupplierPartnerCode”: “None”,“SupplierPreferredOrNot”: null, “SupplierRank”: “7”, “SupplierRegion”:“AMERICAS”}, {“ContactEmail”: “None”, “SupplierCategory”: “½ pipe nipplevalves”, “SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MROSUPPLIES”, “SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “CONTROL VALVES”, “SupplierCountry”: “UNITEDSTATES OF AMERICA”, “SupplierName”: “XYZ PIPE AND SUPPLY CO”,“SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “8”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “CONTROL VALVES”, “SupplierCountry”: “UNITEDSTATES OF AMERICA”, “SupplierName”: “WXY USA INC”,“SupplierPartnerCode”: “None”, “SupplierPreferredOrNot”: null,“SupplierRank”: “9”, “SupplierRegion”: “AMERICAS”}, {“ContactEmail”:“None”, “SupplierCategory”: “½ pipe nipple valves”,“SupplierCategoryAIL1”: “MRO”, “SupplierCategoryAIL2”: “MRO SUPPLIES”,“SupplierCategoryAIL3”: “PIPES, VALVES & FITTINGS”,“SupplierCategoryAIL4”: “CONTROL VALVES”, “SupplierCountry”: “UNITEDSTATES OF AMERICA”, “SupplierName”: “XYZ CORP”, “SupplierPartnerCode”:“None”, “SupplierPreferredOrNot”: null, “SupplierRank”: “10”,“SupplierRegion”: “AMERICAS”}]}

Referring to FIG. 3K, a table 300K depicting item to category mapping isshown according to an embodiment of the invention. The category mappingis performed through a bot by processing a clustering script to map itemto category. Even in case of a new item not previously processed, thescript is configured to identify characteristics associated with theitem and map it to the category or create a new category by processingthrough the AI engine.

Referring to FIG. 4 , a flow diagram 400 of a negotiation strategyexecuted by the auto-negotiator is shown in accordance with an exampleembodiment of the present invention. In an example embodiment, the autonegotiator in step 401 checks for availability of a feedback based on aninput received, if it is available then in step 402 the best strategy isrecommended as per the input. The System processes all historicalnegotiation data and strategies such as savings and turnaround time,along with current conditions such as market and rate predictions, tosuggest the best strategy. If the feedback is not available, then instep 403 check how many suppliers are available for negotiation. Ifsuppliers are less than or equal to 3 then in step 404 it is checked ifthe suppliers know each other, if they know each other then in step 405a conceal auction is recommended. The concealed auction can be anauction like a concealed Dutch Auction. In concealed Dutch auction, thenumber and names of all competitors are concealed from participants. Ifthe suppliers don't know each other then in step 406, it is checked withthere are clear measurable cost drivers, if yes then in step 407 lowestPrice point (LPP) based negotiation is recommended, else in step 408 itis checked if should cost is available. If should cost is available,then in step 409 should cost based negotiation is recommended, else in410 face to face negotiation is conducted. In Should Cost negotiation,the negotiation is enabled based on information related to prevailingcosts. If in step 403 it is determined that the number of suppliers are4 or more then in step 411, RFx based on target saving is recommendedand in step 412 award scenario is checked based on bid optimization. Instep 413, it is checked if saving is achieved then in step 414, the autocreation of contract is initiated with the selected supplier. If savingis not achieved, then in step 415 boosting RFx events for future costreduction. In step 416 it is checked if savings is achieved, if yes thenauto creation of contact is initiated with the selected supplier elseshould cost based negotiation is recommended. The negotiations data isprovided as a feedback to a negotiation strategy database 417 forconducting negotiations.

In an exemplary embodiment the auto-negotiator processes data scriptsthrough the AI engine for predicting recommendation with logisticregression and median calculations. The data scripts adapt processinglogic to each category enabling changing decision parameters and toolrecommendations over time. The AI engine prediction caters to thepossibilities of being selected by a user and average real savings. Therecommendations predict one or more optimum negotiation approach(tender, auction, face to face etc.,) and most effective negotiationlevers/parameters (LPP, should cost, benchmarking).

In an embodiment the invention provides a recursive loop of performingcontinuous negotiation cycle with suppliers is derived from aprobabilistic based data modelling configured to auto-set a target foran object and allowing a user to meet targets by lowering proposal toachieve maximum savings.

In an exemplary embodiment the present invention provides a categorymanagement system for supply chain operations. The category managementsystem includes a plurality of task tools configured for triggering theat least one task based on a received demand from one or more datasources. The system also includes one or more trend indicatorsconfigured for providing the actionable insights to the user through thedashboard of the workbench application user interface. The actionableinsights include category spend monitoring data, category classificationand positioning data, supply market analysis data, supplier spendmonitoring data, cost driver data, strategy data, opportunityidentification data, risk assessment data. The system includes the AIengine coupled to the processor and configured for tracking andmonitoring a plurality of parameters driving one or more supply chainoperations. The plurality of parameters includes category strategies,key projects, supplier risk factors, contract performance indicator, andcosts. The system analyzes trends including supply, demand and pricingtrends in supply chain.

In one embodiment the category management system includes an organizerconfigured to generate a set of quantitative and qualitative data on thedashboard to analyze trends in supply chain. The quantitative dataincludes market indices, commodity prices, stock price of supplier,delivery turn-around time (TAT), changes in market shares, demand andsupply forecasts, expected lead times, savings expectations andtracking, compliance, percentage of managed spend, benchmarks for spendand prices, and should cost models with cost evolution. The qualitativedata includes newsfeeds, about innovation, litigation, Merger andAcquisition, spin-offs, bankruptcy, entry and exit of key executives,path breaking innovation, supply shocks, strategic changes.

In an exemplary embodiment the category management system includes a subnetwork having at least one server configured to process a plurality ofbackend scripts generated by the bot to identify a relevant script for arecommended strategy and a control unit configured to process thestrategy based on the identified relevant script for automating at leastone task. The control unit selects an Artificial Intelligence baseddynamic processing logic using the bot to reduce the processing time ofthe task.

Referring to FIG. 5A-5H, a category workbench application user interfacefor autonomous sourcing and category management is shown in accordancewith an embodiment of the invention. Based on a demand raised by an autotrigger such as expiring contract, or project module, or a requisitionfor sourcing through an entity team like HR team, marketing team orPlant maintenance team, a sourcing module is triggered through thecategory workbench user interface. The object for sourcing such as itemor Services is assessed through user interface for should cost model,trends of raw material and price benchmarks of the objects. If theassessment through the workbench determines there are no significantcost saving and the supplier of the expiring contract is already thelowest price, then auto renewal of the contract is initiated. Thecontract is reframed automatically for latest clauses. If the assessmentdetermines significant cost saving, then an autonomous sourcing processis triggered and monitored through the category workbench userinterface. FIG. 5A shows an interface 500A, with actionable insightsrelated to contracts, Projects, RFx, Category Spend, Business unitSpend, spend region, Supplier Spend, actual vs budgeted spend, top costdrivers, supplier competitiveness and Strategies on the same interface.FIG. 5B shows an interface 500B, with details of the spend profileincluding spend by category, spend by region, spend by business unit,spend by Payment terms, contracted Vs non-contracted spend by categoryand spend trends. The supplier profile includes information about topsuppliers with supplier category spend, supplier region, business unitspend, payment terms spend.

FIG. 5C shows an interface 500C, assessing supply market providingimpact of certain parameters like impact of product, impact on business,impact on growth, level of spend, importance for function etc. Theseparameters are weighted and scored on the interface. The supply marketinterface assesses switching costs, impact of buyer, supplier,availability of substitutes, threat of new Entrants, competitive rivalryetc.

FIG. 5D shows an interface 500D, providing insight into cost driverssuch as raw material, labor cost, Chemicals, fuel costs, packaging,depreciation, transport and other categories with probability ofnegotiation. The system is configured to assess the historical data withimpact parameters associated with market indices to predict theprobability score.

In an embodiment the category workbench interface provides forecastinformation related to objects such as forecasted price of oil from aregion.

Referring to FIG. 5E, the category workbench interface 500E providesinsight into market indices for items such as metal and metal productsincluding iron and steel. Further, it also provides insight into coal,diesel, Aluminum, Paper, wages, Iron, Gold and silver at the sameinstant through graphical representations.

Referring to FIG. 5F, a category workbench user interface 500F providinginsight into strategy is shown in accordance with an embodiment of theinvention. The strategy includes sourcing strategy, Optimization, demandmanagement, make vs buy, demand reduction, leakage management,consumption policy, Supplier acquisition, Socio-political managementetc. The strategy provides insight into addressable spend, savingspotential, ease of implementation and execution timelines, Potentialbenefits. Based on the identified strategy the interface enables flip toproject with ease. The projects overview on the interface 500G is shownin FIG. 5G. The user interface also provides insight into supplierprofile with collated data across multiple parameters for enabling easein decision. The interface also provides reporting information on thedashboard with Product roadmap and details of Project reports, spendanalysis report, contract details, savings across category, spend,region, etc.

Referring to FIG. 5H, a category workbench application user interface500H showing spend and saving insight is provided in accordance with anembodiment of the invention. The interface at the same instant providesinformation about category spend, managed spend, targeted savings,related documents, category positioning and spend with impact, spend bysupplier segment etc. The user interface further provides insight intotarget savings and ease of implementation for category such as IT, MRO,marketing etc. The target savings and ease of implementation insightalso provides spend consolidation, Contract renegotiation, SKUrationalization and logistics optimization overview.

In an exemplary embodiment, the category workbench application userinterface may enable cognitive computing to improve interaction betweena user and the supply chain application(s). The intelligent interfaceprovides insight into dynamically changing parameters such as keyinformation obtained from live newsfeeds. The AI engine processes thenewsfeed to draw relevance from the content and provide actionableinsight to a user. Alternately, the system is configured to assignweights to type of news feeds and impact of certain news on supply chainto auto rate the scenario and modify the sourcing strategy or supplierrecommendation for executing the recommended strategy. The AI engineprocesses the newsfeed based on a data script configured forunderstanding the content and relating it to impact characteristics inreal time. For Eg., if the supplier is involved in certain type oflitigation in a Jurisdiction which may impact the execution of strategy,then the AI engine shall automatically exclude the supplier from therecommended suppliers list depending on the parameters preferred by auser. The live news feed providing such information is extremely usefulin ensuring risk free sourcing process for an entity. Further, in anadvantageous aspect, the cognitive aspect of the invention enables acategory manager to override an auto assessment by the AI engine ifrequired.

In an exemplary embodiment, the present invention may be a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention. The media has embodiedtherein, for instance, computer readable program code (instructions) toprovide and facilitate the capabilities of the present disclosure. Thearticle of manufacture (computer program product) can be included as apart of a computer system/computing device or as a separate product.

The computer readable storage medium can retain and store instructionsfor use by an instruction execution device i.e. it can be a tangibledevice. The computer readable storage medium may be, for example, but isnot limited to, an electromagnetic storage device, an electronic storagedevice, an optical storage device, a semiconductor storage device, amagnetic storage device, or any suitable combination of the foregoing. Anon-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a hard disk, a random accessmemory (RAM), a portable computer diskette, a read-only memory (ROM), aportable compact disc read-only memory (CD-ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a digitalversatile disk (DVD), a static random access memory (SRAM), a floppydisk, a memory stick, a mechanically encoded device such as punch-cardsor raised structures in a groove having instructions recorded thereon,and any suitable combination of the foregoing. A computer readablestorage medium, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The foregoing is considered as illustrative only of the principles ofthe disclosure. Further, since numerous modifications and changes willreadily occur to those skilled in the art, it is not desired to limitthe disclosed subject matter to the exact construction and operationshown and described, and accordingly, all suitable modifications andequivalents may be resorted to that which falls within the scope of theappended claims.

What is claimed is:
 1. A system for Category management, the systemcomprises: a category workbench application user interface configured togenerate a plurality of data patterns related to one or more objectcategories for providing actionable insights to a user through at leastone dashboard of the interface; an intelligent bot configured forinjecting the data patterns into a recommended strategy for generatingat least one object characteristic data set; and a processor configuredto process historical data from a data lake and the objectcharacteristic data set to identify one or more suppliers for executingthe recommended strategy, wherein the actionable insight includes a setof qualitative and quantitative data generated by processing ofhistorical data from the data lake to analyze trends in supply chain forcategory management by enabling execution of at least one task.
 2. Thesystem of claim 1 further comprises: generating a code for therecommended strategy through prediction analysis by processing thehistorical data from a data lake.
 3. The system of claim 2 wherein thecategory workbench application interface triggers a plurality ofpredictive data models to identify the one or more object categories. 4.The system of claim 1 wherein the bot is configured to generate backendscripts based on the recommended strategy for injecting the aggregateddata using AI based dynamic processing logic to generate the objectcharacteristic data set.
 5. The system of claim 1 further comprises aplurality of task tools configured for triggering the at least one taskbased on a received demand from one or more data sources.
 6. The systemof claim 1 further comprises one or more trend indicators configured forproviding the actionable insights to the user through the dashboard ofthe workbench.
 7. The system of claim 1 wherein the actionable insightsinclude category spend monitoring data, category classification andpositioning data, supply market analysis data, supplier spend monitoringdata, cost driver data, strategy data, opportunity identification data,risk assessment data.
 8. The system of claim 1 further comprises AIengine coupled to the processor and configured for tracking andmonitoring a plurality of parameters driving one or more supply chainoperation wherein the plurality of parameters includes categorystrategies, key projects, supplier risk factors, contract performanceindicator, and costs.
 9. The system of claim 1 wherein the quantitativedata includes market indices, commodity prices, stock price of supplier,delivery turn-around time (TAT), changes in market shares, demand andsupply forecasts, expected lead times, savings expectations andtracking, compliance, percentage of managed spend, benchmarks for spendand prices, and should cost models with cost evolution.
 10. The systemof claim 1 wherein the qualitative data includes newsfeeds, aboutinnovation, litigation, Merger and Acquisition, spin-offs, bankruptcy,entry and exit of key executives, path breaking innovation, supplyshocks, strategic changes.
 11. The system of claim 1 wherein the trendsinclude supply, demand and pricing trends in supply chain.
 12. Thesystem of claim 1 further comprises: a sub network having at least oneserver configured to process a plurality of backend scripts generated bythe bot to identify a relevant script for the at least one recommendedstrategy; and a control unit configured to process the at least onestrategy based on the identified relevant script for automating at leastone task, wherein the control unit selects an Artificial Intelligencebased dynamic processing logic using the bot to reduce the processingtime of the task.
 13. The system of claim 1 wherein the categoryworkbench application user interface is configured to provide theactionable insights into the one or more data patterns being selectableto trigger an application associated with each of the one or more datapatterns and enable the selected data pattern to be seen within theapplication and providing details on spend category, supplier regionsspend, actual/vs target spend, top cost drivers and strategies.
 14. Amethod of Category management, the method comprises: generating aplurality of data patterns related to one or more object categories forproviding actionable insights to a user through at least one dashboardof a category workbench application user interface; injecting by anintelligent bot, the data patterns related to one or more objectcategories into a recommended strategy for generating at least oneobject characteristic data set; processing historical data from a datalake and the object characteristic data set to identify one or moresuppliers for executing the recommended strategy, and generating a setof quantitative and qualitative data on the dashboard to analyze trendsin supply chain for category management by enabling execution of atleast one task initiated by a user through the interface.
 15. The methodof claim 14 further comprises: generating a code for the recommendedstrategy through prediction analysis by processing the historical datafrom a data lake.
 16. The method of claim 15 further comprises the stepof analyzing historical data through the workbench application interfaceand perform AI based budget predictions and demand aggregation byoverlaying a historical spend data with disparate forecasting modelsbuilt on various data sources available to analyze spend and pricingtrends.
 17. The method of claim 15 wherein the bot is configured togenerate backend scripts based on the recommended strategy for injectingthe aggregated data using AI based dynamic processing logic to generatethe object characteristic data set.
 18. The method of claim 15 furthercomprises: initiating automated tactical execution process based on therecommended strategy wherein the recommendation is auto-flipped intoprojects with a pre-populated responsibility assignment matrix, asavings target, one or more impacted categories and a supplier data. 19.The method of claim 18 further comprises: encapsulating one or moreawarding scenario on the category workbench application user interfaceby the bot wherein an AI engine incorporates rules and targetconstraints including preferable number of suppliers, preferentialawards to incumbent suppliers, minimum lead times, and savings goals toautomatically arrive at a most efficient cost for executing recommendedstrategy.
 20. A computer program product for category management insupply chain management application of a computing device with memory,the product comprising: a computer readable storage medium readable by aprocessor and storing instructions for execution by the processor forperforming a category management method, the method comprises:generating a plurality of data patterns related to one or more objectcategories for providing actionable insights to a user through at leastone dashboard of a category workbench application user interface;injecting by an intelligent bot, the data patterns related to one ormore object categories into a recommended strategy for generating atleast one object characteristic data set; processing historical datafrom a data lake and the object characteristic data set to identify oneor more suppliers for executing the recommended strategy, and generatinga set of quantitative and qualitative data on the dashboard to analyzetrends in supply chain for category management by enabling execution ofat least one task initiated by a user through the interface.